AUTHOR=Wu Haoran , Xu Kunyun , Liu Centao , Ge Heng’an , Yan Jianfeng 

TITLE=Global research trends and emerging themes in osteosarcoma metabolomics: a bibliometric and visualization analysis

JOURNAL=Frontiers in Molecular Biosciences

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2025.1608743

DOI=10.3389/fmolb.2025.1608743

ISSN=2296-889X

ABSTRACT=IntroductionThis study conducts a systematic bibliometric analysis of the global research landscape of metabolomics in osteosarcoma, aiming to identify research trends, knowledge structures, and emerging directions in the field.MethodsPublications related to osteosarcoma and metabolomics were retrieved from the Web of Science Core Collection. Bibliometric analysis was performed using CiteSpace, VOSviewer, and Bibliometrix to examine publication trends, geographic and institutional collaborations, author networks, keyword co-occurrence, clustering, and co-citation patterns.ResultsA total of 1,188 eligible articles published between 1995 and 2024 were included. The analysis revealed significant growth in publications and citations over the past decade, with China being the leading contributor. High-frequency keywords such as “biomarkers,” “prognosis,” and “chemoresistance” indicated a strong research focus on tumor progression and treatment resistance. Clustering and burst detection highlighted emerging topics, including extracellular vesicles, microRNAs, and immune metabolism. Co-citation analysis established a knowledge foundation centered on molecular profiling and translational research, with growing interest in spatial and single-cell metabolomics reflecting a shift toward high-resolution metabolic characterization.DiscussionThis bibliometric study underscores the evolving research priorities and methodological advancements within osteosarcoma metabolomics. It offers a comprehensive reference for researchers to understand thematic evolution, recognize knowledge gaps, and foster the development of more precise and integrated metabolic strategies for improving diagnosis and treatment.